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Image analysis and statistical modelling for measurement and quality assessment of ornamental horticulture crops in glasshouses\ud

By Nicholas R. Parsons, R. N. Edmondson and Yu Song


Image analysis for ornamental crops is discussed with examples from the bedding plant industry. Feed-forward artificial neural networks are used to segment top and side view images of three contrasting species of bedding plants. The segmented images provide objective measurements of leaf and flower cover, colour, uniformity and leaf canopy height. On each imaging occasion, each pack was scored for quality by an assessor panel and it is shown that image analysis can explain 88.5%, 81.7% and 70.4% of the panel quality scores for the three species, respectively. Stereoscopy for crop height and uniformity is outlined briefly. The methods discussed here could be used for crop grading at marketing or for monitoring and assessment of growing crops within a glasshouse during all stages of production

Topics: HA, SB
Publisher: Elsevier Ltd.
Year: 2009
OAI identifier:

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  12. (1994). Image Analysis for the Biological Sciences. doi
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  14. (2004). Graphical tracking systems revisited: a practical approach to computer scheduling in horticulture.
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  20. (1967). Some Methods for classification and analysis of multivariate observations. In:
  21. (2006). A generalized estimating equation method for fitting autocorrelated ordinal score data with an application in horticultural research. doi
  22. (2004). Statistical methods for improving pot-plant quality and robustness. Unpublished PhD thesis.
  23. (2007). Core Team doi
  24. (1994). Neural networks and related methods for classification. doi
  25. (1997). Regression analysis: Theory, Methods and Applications. doi
  26. (2007). Surface modelling of plants from stereo images. In: doi
  27. (2008). Modelling and analysis of plant image data for crop growth and monitoring in horticulture. Unpublished PhD thesis.
  28. (1974). Cross-validatory choice and assessment of statistical predictions.
  29. (2005). Measurement of plants by stereo vision for agricultural applications. In:
  30. (1996). Computer vision system for on-line sorting of pot plants using an artificial neural network classifier. doi
  31. (1981). Comparison of discrimination techniques applied to a complex data set of head injured patients. doi
  32. (2001). Neural networks used for classification of potted plants.
  33. (1991). Determination of the physiological state of potted plants and cut flowers by modulated chlorophyll fluorescence.
  34. (2000). The Nature of Statistical Learning Theory. doi
  35. (2008). Evolving neurocomputing systems for horticulture applications. doi
  36. (1998). A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops. doi

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